Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ∼350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ∼100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.

Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event was mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 64.5 % to the PM_2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Tianjin city, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, relative humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 278.2 m and PM_2.5 increased more than 20 µg m⁻³ due to aerosol feedback. It was also shown that black carbon (BC) absorption has significant impacts on meteorology and air quality changes, indicating more attention should be paid to BC from both air pollution control and climate change perspectives.

An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM_2.5 predictions reduced at over 90% of the stations and AOD errors reduced at 100% of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

The colocation of clouds and smoke over the southeast Atlantic Ocean during the southern African biomass burning season has numerous radiative implications, including microphysical modulation of the clouds if smoke is entrained into the marine boundary layer. NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign is studying this system with aircraft in three field deployments between 2016 and 2018. Results from ORACLES-2016 show that the relationship between cloud droplet number concentration and smoke below cloud is consistent with previously reported values, whereas cloud droplet number concentration is only weakly associated with smoke immediately above cloud at the time of observation. By combining field observations, regional chemistry–climate modeling, and theoretical boundary layer aerosol budget equations, we show that the history of smoke entrainment (which has a characteristic mixing timescale on the order of days) helps explain variations in cloud properties for similar instantaneous above-cloud smoke environments. Precipitation processes can obscure the relationship between above-cloud smoke and cloud properties in parts of the southeast Atlantic, but marine boundary layer carbon monoxide concentrations for two case study flights suggest that smoke entrainment history drove the observed differences in cloud properties for those days. A Lagrangian framework following the clouds and accounting for the history of smoke entrainment and precipitation is likely necessary for quantitatively studying this system; an Eulerian framework (e.g., instantaneous correlation of A-train satellite observations) is unlikely to capture the true extent of smoke–cloud interaction in the southeast Atlantic.

Planned geostationary satellites will provide aerosol optical depth (AOD) retrievals at high temporal and spatial resolution which will be incorporated into current assimilation systems that use low‐Earth orbiting (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) AOD. The impacts of such additions are explored in a real case scenario using AOD from the Geostationary Ocean Color Imager (GOCI) on board of the Communication, Ocean, and Meteorology Satellite, a geostationary satellite observing northeast Asia. The addition of GOCI AOD into the assimilation system generated positive impacts, which were found to be substantial in comparison to only assimilating MODIS AOD. We found that GOCI AOD can help significantly to improve surface air quality simulations in Korea for dust, biomass burning smoke, and anthropogenic pollution episodes when the model represents the extent of the pollution episodes and retrievals are not contaminated by clouds. We anticipate future geostationary missions to considerably contribute to air quality forecasting and provide better reanalyses for health assessments and climate studies.